Two-dimensional (2D) Dirac materials have attracted intense research efforts due to their promise for applications ranging from field-effect transistors and low-power electronics to fault-tolerant quantum computation. One key challenge is to fabricate 2D Dirac materials hosting Dirac electrons. Here, monolayer germanene is successfully fabricated on a Ag2Ge surface alloy. Scanning tunneling spectroscopy measurements revealed a linear energy dispersion relation. The latter was supported by density functional theory calculations. These results demonstrate that monolayer germanene can be realistically fabricated on a Ag2Ge surface alloy. The finding opens the door to exploration and study of 2D Dirac material physics and device applications.Positron emission tomography (PET) remains the gold standard for quantitative imaging of the cerebral metabolic rate of oxygen (CMRO2); however, it is an invasive and complex procedure that requires accounting for recirculating [15O]H2O (RW) and the cerebral blood volume (CBV). This study presents a non-invasive reference-based technique for imaging CMRO2 that was developed for PET/magnetic resonance imaging (MRI) with the goal of simplifying the PET procedure while maintaining its ability to quantify metabolism. The approach is to use whole-brain (WB) measurements of oxygen extraction fraction (OEF) and cerebral blood flow (CBF) to calibrate [15O]O2-PET data, thereby avoiding the need for invasive arterial sampling. Here we present the theoretical framework, along with error analyses, sensitivity to PET noise and inaccuracies in input parameters, and initial assessment on PET data acquired from healthy participants. Simulations showed that neglecting RW and CBV corrections caused errors in CMRO2 of less than ±10% for changes in regional OEF of ±25%. These predictions were supported by applying the reference-based approach to PET data, which resulted in remarkably similar CMRO2 images to those generated by analyzing the same data using a modeling approach that incorporated the arterial input functions and corrected for CBV contributions. Significant correlations were observed between regional CMRO2 values from the two techniques (slope = 1.00 ± 0.04, R 2 &gt; 0.98) with no significant differences found for integration times of 3 and 5 min. In summary, results demonstrate the feasibility of producing quantitative CMRO2 images by PET/MRI without the need for invasive blood sampling.As the machinery of artificial intelligence matures in recent years, there has been a surge in applying machine learning (ML) techniques for material property predictions. Artificial neural network (ANN) is a branch of ML and has gained increasing popularity due to its capabilities of modeling complex correlations among large datasets. The interfacial thermal transport plays a significant role in the thermal management of graphene-pentacene based organic electronics. In this work, the thermal boundary resistance (TBR) between graphene and pentacene is comprehensively investigated by classical molecular dynamics simulations combined with the ML technique. The TBR values along thea,bandcdirections of pentacene at 300 K are 5.19 ± 0.18 × 10-8m2K W-1, 3.66 ± 0.36 × 10-8m2K W-1and 5.03 ± 0.14 × 10-8m2K W-1, respectively. Different architectures of ANN models are trained to predict the TBR between graphene and pentacene. Two important hyperparameters, i.e. network layer and the number of neurons are explored to achieve the best prediction results. It is reported that the two-layer ANN with 40 neurons each layer provides the optimal model performance with a normalized mean square error loss of 7.04 × 10-4. Our results provide reasonable guidelines for the thermal design and development of graphene-pentacene electronic devices.Prior-image-based reconstruction (PIBR) methods are powerful in reducing radiation dose and improving image quality for low-dose CT. Besides anatomical changes, the prior and current images can also have different attenuation due to different scanners or the same scanner but with different x-ray beam quality (e.g., kVp setting, beam filtration) during data acquisitions. PIBR is challenged in such scenarios with attenuation mismatched prior. In this work, we investigate a specific PIBR method, called statistical image reconstruction using normal dose image induced nonlocal means regularization (SIR-ndiNLM), to address PIBR with such attenuation mismatched prior and achieve quantitative low-dose CT imaging. We proposed two corrective schemes for the original SIR-ndiNLM method, 1) a global histogram matching approach and 2) a local attenuation correction approach, to account for the attenuation differences between the prior and current images in PIBR. We validated the efficacy of the proposed schemes using images acquired from dual-energy CT scanners to emulate attenuation mismatches. Meanwhile, we utilized different CT slices to emulate anatomical mismatches/changes between the prior and the current low-dose images. We observed that the original SIR-ndiNLM introduces artifacts to the reconstruction when using attenuation mismatched prior. https://www.selleckchem.com/products/jh-re-06.html Furthermore, we found that larger attenuation mismatch between the prior and current images results in more severe artifacts in the SIR-ndiNLM reconstruction. Our proposed two corrective schemes enabled SIR-ndiNLM to effectively handle attenuation mismatch and anatomical changes between two images and successfully eliminate the artifacts. We demonstrated that the proposed techniques permit SIR-ndiNLM to leverage the attenuation mismatched prior and achieve quantitative low-dose CT reconstruction from both low-flux and sparse-view data acquisitions. This work permits robust and reliable PIBR for CT data acquired using different beam settings.This study aims to develop a computer-aided diagnosis (CADx) scheme to classify between benign and malignant ground glass nodules (GGNs), and fuse deep leaning and radiomics imaging features to improve the classification performance.
We first retrospectively collected 513 surgery histopathology confirmed GGNs from two centers. Among these GGNs, 100 were benign and 413 were malignant. All malignant tumors were stage I lung adenocarcinoma. To segment GGNs, we applied a deep convolutional neural network and residual architecture to train and build a 3D U-Net. Then, based on the pre-trained U-Net, we used a transfer learning approach to build a deep neural network (DNN) to classify between benign and malignant GGNs. With the GGN segmentation results generated by 3D U-Net, we also developed a CT radiomics model by adopting a series of image processing techniques, i.e. radiomics feature extraction, feature selection, synthetic minority over-sampling technique, and support vector machine classifier training/testing, etc.